International Journal of Artificial Intelligence, Data Science, and Machine Learning
Auto-BI Frameworks Powered by Generative Artificial Intelligence for Scalable Self-Service Data Analytics in Large Organizations
The exponential growth of enterprise data has created significant challenges for organizations seeking to derive timely and actionable insights from complex datasets. The conventional Business Intelligence (BI) systems are typically based on expert analysts, formal query languages, and ad hoc dashboard development, which may reduce accessibility and reduce the speed of decision-making. The current developments in Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) offer fresh possibilities to make the traditional BI systems automated, intelligent, and easy to use. The technologies make it possible to interact with data naturally, create insights automatically and dynamically visualizing data, which considerably enhance analytical efficiency. The proposed research paper suggests a Self-service Data analytics-driven Auto-BI framework, which is driven by Generative Artificial Intelligence, to assist in scaling self-service data analytics in large organizations. The proposed architecture will incorporate several layers, such as data sources, data integration pipelines, generative AI analytics engine, BI service elements, and user interaction interfaces. Through the framework, users can enter queries that are in natural language, which are automatically converted to an analytical query via AI-based Natural Language to SQL systems. The system also includes automated dashboard creation, smart data exploration and predictive analytics capabilities to improve the analytical functions of enterprise BI environments. Experimental results and industrial standards show that AI-based generative analytics engines can be of substantial benefit in terms of analytical performance: the query-to-insight latency can be reduced, reporting efficiency can be maximized, and the productivity of the decision-making process can be boosted. The suggested Auto-BI architecture shows how generative AI technologies can make the data more democratic and less reliant on the expertise of the individual technical team, and how an analytical solution can scale to the needs of the modern data-oriented organizations.
Generative Artificial Intelligence (Genai)
Automated Business Intelligence (Auto-BI)
From Alert Floods to Action: Correlated Telemetry for High-Volume Banking Systems
High-volume banking systems (e.g., payments, digital channels, core banking) generate many types of observability data (metrics, logs, traces, topology events), and the sheer volume and variety of signals mean that simple per-service thresholds will frequently trigger waves of alerts. These alert floods increase the cost of being on call, scatter evidence across multiple tools, and slow diagnosis and mitigation, which is particularly risky for critical transaction flows that span authorization, fraud scoring, ledger posting, and customer notifications. This paper proposes a correlated-telemetry approach, which first normalizes multiple signal types using OpenTelemetry-style semantic conventions, second correlates alerts across a service-dependency graph and over sliding time windows, and third produces a compact, auditable incident record with ranked candidate root causes and runbook suggestions. Real banking telemetry is typically private and regulated, so we evaluate the approach with a synthetic replay based on publicly-inspectable statistical priors taken from Kaggle and UCI financial risk datasets (rarity, class imbalance, burstiness), which we map into incident frequency, weak-signal emission, duplication, and noise parameters to enable reproducible experiments without exposing proprietary telemetry. Across 30 randomized replays, the proposed method results in consistent improvements, including a 61.6% (±1.2) decrease in non-actionable pages, a 23.3±0.3 minute increase in median pre-escalation lead time, a 35.5% (±0.5) decrease in mean time to restore, a 39.1% (±2.7) decrease in high-severity incidents, and a 7.8±0.3 basis point increase in impact-weighted availability. The paper also includes an ablation study that separates the unique contributions of weak-signal inclusion and evidence-based triage, and it describes a shadow-mode validation protocol that banks and other regulated industries (healthcare, telecom, energy, public sector) can use to validate this approach using de-identified, aggregated telemetry.
Federated Learning Framework for Privacy-Preserving Cross-Bank Fraud Detection
The detection of financial fraud continues to be an important problem to be solved for all major international banking institutions due to projected annual fraud loss figures over $32B. The primary means to detect fraud effectively, is through the examination of a wide variety of transactions from many different banking organizations. However, due to privacy laws including GDPR and CCPA, as well as competition law related to control of customer data, it is very difficult for banking organizations to share their customers’ transactional data. In this paper we describe a novel federated learning architecture for fraud detection which will enable collaboration among multiple banking institutions to identify fraud without the exchange of customers' private raw transaction data. The proposed method will use a decentralized machine learning architecture where each participant trains its own model using their private dataset and transmits to a centralized coordinating node only encrypted, noisy versions of the model's updates. To ensure the security of the transmitted model updates, we propose to employ secure aggregation protocols, differential privacy mechanisms, and Byzantine-robust aggregation methods to prevent malicious participants from carrying out inference attacks and model poisoning attacks. Experimental results show that our method can achieve a 15 – 30% increase in fraud detection rates relative to stand-alone institutionally-trained models, and are able to find complex cross-institutional patterns indicative of fraudulent behavior. These results also confirm that our method does comply with all relevant regulatory requirements regarding the privacy and sovereignty of customers' data. Thus, this method presents a technical pathway to allow banking consortia to collaborate to counter sophisticated organized crime groups in a manner consistent with applicable data sovereignty, privacy regulations and competitive restrictions.
Behavioral Pattern Analysis for Return Fraud Detection in High-Volume E-Commerce: A Multi-Signal Approach
Return fraud costs U.S. retailers $103 billion annually, yet academic fraud detection literature remains almost entirely focused on payment transaction fraud. This paper presents a multi-signal behavioral analysis framework for return fraud detection that fuses three complementary signal streams: transaction-level features, behavioral history profiles, and natural language processing of customer-supplied return reason text. A late-fusion stacking architecture combines independently trained branch classifiers through a gradient-boosted meta-learner, enabling modular updates as fraud patterns evolve. Cost-sensitive learning, calibrated to the financial value of individual transactions, ensures that the system's optimization objective aligns with actual business impact rather than raw classification accuracy. A five-component evaluation methodology employs temporal validation and example-dependent cost metrics to establish realistic performance benchmarks that account for concept drift and class imbalance inherent to high-volume retail environments. The proposed system targets greater than 90% sensitivity for high-severity fraud categories while maintaining greater than 85% specificity to protect legitimate customer relationships. By integrating NLP analysis of return justification text a signal channel absent from all existing fraud detection literature despite a documented 76% consumer embellishment rate this framework addresses a structural gap in both academic research and commercial practice.
AI-Optimized Symmetry Episode Analytics for Early Detection of High-Utilizers: A Claims-Based Predictive Modelling Framework Using Advanced Machine Learning Models
The continuously increasing medical expenses are now considered as priority for national economy and organizational finance management for strategic emphasis on finances. The increasing concern about economic risks and compounded influence on population health needs to be managed suitably. Managing issues like increasing costs and staff limitations, along with cyber issues require insightful analysis. Perceptions regarding the continuously changing healthcare challenge is critical for supporting claim processing with compliance and effective care. Supporting patients with high-quality healthcare emphasizes on persistent threats. They require practical processes to support in managing associated complexities. Increasing the strength of healthcare claim management and risk handling requires using strategies to manage the challenges. This study involves development of a predictive modeling structure and framework, unifying symmetry episode analytics and ETG/ERG/PEG with supervised learning models. These models enhance the strength to manage compliance and address shrinking margins according to revenue generated. The paper discusses information regarding Symmetry episode analytics combination with intelligence (AI) and learning (ML), for effective therapy delivery and cost reduction. The outcomes involve demonstration of AI-streamlined episode analytics for identification of high-utilizers proactively and empower healthcare management for targeted interventions therapeutically.
Attention-Based Driver Behavior Monitoring System Using Multi-Modal Deep Learning
Road traffic accidents resulting from driver distractions, drowsiness, and inattentiveness are a significant global concern. To address this problem, It proposes an "Attention-Based Driver Behavior Monitoring System Using Multi-Modal Deep Learning" for real-time classification of drivers' states. The proposed framework uses visual features extracted from in-cabin camera images along with behavioral features for more robust detection. CNN are used for spatial feature extraction, while BiLSTM networks are used in temporal behavior. Additionally, an adaptive attention mechanism is proposed for more robust feature modeling. It performs with standard metrics. From the experimental results, it is clear that the proposed attention-based framework achieved an accuracy of 96.8%, better than conventional models, including CNN and CNN+LSTM. The inclusion of the attention-based feature weighting process also minimizes false alarms and increases the sensitivity of the system for the detection of safety-critical conditions such as drowsiness and distraction. The proposed architecture also ensures computational efficiency for the deployment of the system in ADAS in the context of transportation systems, and the results validate the effectiveness of the fusion of the spatial-temporal modeling and adaptive multi-modal fusion for the development of intelligent driver behavior monitoring in smart transportation systems.
Advanced Driver Assistance Systems (ADAS)
Optimizing Cloud-Native Micro service Architecture: Design Principles, Scalability, and Operational Resilience
The rise of cloud-native microservice architecture has redefined how organizations design, deploy, and manage large-scale applications in distributed computing environments. This research examines the evolution, design principles, and optimization strategies underpinning cloud-native systems, emphasizing their scalability, resilience, and operational efficiency. By decomposing applications into independently deployable services, microservice architecture enables agility and continuous delivery, yet introduces new challenges in orchestration, observability, and security. Through a review of scholarly literature and industry case studies published up to 2022, this study explores the theoretical foundations of modular design, decentralized control, and adaptive scalability that define cloud-native systems. It further evaluates technologies such as Kubernetes, Docker, and service mesh frameworks that facilitate automated deployment and fault tolerance. Findings highlight that successful cloud-native adoption requires a balanced integration of automation, DevOps practices, and resilience engineering, supported by cultural and organizational transformation. The paper concludes by proposing a conceptual framework for sustainable optimization of microservices, identifying future directions involving AI-driven orchestration, edge computing, and AIOps-enabled self-healing systems. This study contributes to the growing body of knowledge on cloud-native computing by synthesizing empirical evidence and theoretical insights to guide future research and practical implementation.
AI-Driven Development Lifecycle (AI-DLC): Reimagining Software Engineering for the AI Era
The software industry stands at an inflection point. Artificial intelligence is no longer a peripheral tool it is becoming a central collaborator in how software is conceived, built, and operated. Yet most organizations are failing to capture the full potential of AI in their development workflows, trapped between two anti-patterns: over-reliance on autonomous AI and under-utilization of AI for narrow tasks. The AI-Driven Development Lifecycle (AI-DLC) is a transformative methodology introduced by AWS in 2025 that reimagines software engineering by positioning AI as a core collaborator throughout the entire development process. Based on over 100 customer experiments across industries and geographies, AI-DLC has demonstrated 10–15x productivity gains, 40–60% improvement in development velocity, and 40–60% reduction in defects, with verified ROI of 300–500% within 12 months [1][2].
Blockchain Technology Explained: A Comprehensive Overview
Traditional network security mechanisms, such as firewalls, intrusion detection systems, and cryptographic protocols, are increasingly inadequate against sophisticated cyberattacks in modern distributed environments. As architectures like the Internet of Things (IoT) and cloud computing expand, ensuring data integrity, privacy, and trustworthy management becomes more complex. In this context, blockchain technology offers a promising paradigm for strengthening network security through decentralization, immutability, and cryptographic consensus. This paper explores blockchain's role in bolstering data security, emphasizing integrity, transparency, and immutability within digital systems. It reviews real-world uses in secure data sharing and identity management, demonstrating how these address modern threats, while employing a qualitative approach to assess existing solutions—their strengths alongside drawbacks like technological immaturity, high costs, complexity, and regulatory obstacles. Ultimately, it posits that effective deployment hinges on tailoring to organizational contexts, defining precise goals, and overcoming these challenges. This study offers a thorough examination of blockchain technology, covering its evolution, structure, core mechanics, constraints, and diverse applications. It delves into foundational traits like decentralization, immutability, and transparency, alongside key concepts, use cases, and ongoing issues, wrapping up with an evaluation of its current landscape, promising horizons, and primary barriers to broad uptake.
Closing the Loop: Agentic AI for Continuous Vulnerability Detection, Validation, and Remediation
In recent years, new agile models of software development have emerged that promote iterative and continuous integration and deployment of software solutions. This increases complexity and raises the attack surface of software systems. Although there are many security analysis solutions that target vulnerability analysis of software solutions, these solutions and others have mainly addressed identification and lackingly address remediation and post-fixing validation that remains largely an expensive and error-prone process. This paper describes an end-to-end agentic AI system designed to complete the security loop by linking continuous vulnerability scanning and remediation suggestions and validation after remediation is applied. This model combines a collection of cooperative individual AI agents that are orchestrated to jointly analyze source code, third-party library dependencies and execution environment artifacts in CI/CD pipelines. This approach is distinct from prior CVE-fix-based solutions as it tracks vulnerability lifecycles while iterating over code and scanning to contextualize vulnerabilities. Additionally, our approach leads to a set of actionable metrics that are one step away from remediation time – for example: true positive persistence; false positive resolution (on per-file-basis); and remediation latency/accuracy. Through experiments on real open-source code repositories integrated into CI/CD pipelines, the evaluation demonstrates that the proposed approach significantly reduces remediating time, redundant vulnerabilities and automation uncertainty in security analyses. With the integration of vulnerability management into a developer’s automation of software operation, this new agentive AI-for-security framework is unique compared to previous DevSecOps by also using an epistemic frame for securing software.
Bias Detection and Fairness in CRM-Based AI Models
Customer Relationship Management (CRM) platforms increasingly rely on Artificial Intelligence (AI) models to automate decision-making across sales, service, marketing, and support operations. These models influence critical business outcomes such as lead prioritization, credit eligibility, customer retention strategies, case routing, and service prioritization. However, the growing adoption of AI in CRM systems introduces significant risks related to algorithmic bias and fairness. Bias in CRM-based AI models can lead to discriminatory outcomes, reduced customer trust, regulatory non-compliance, and reputational damage. This paper presents a comprehensive analysis of bias sources in CRM-based AI models, techniques for bias detection, fairness metrics, and mitigation strategies. It further discusses governance frameworks and platform-specific considerations for ensuring responsible and ethical AI deployment in modern CRM ecosystems.
AI-Assisted CRM Agents for Sales, Service, and Support Operations
Customer Relationship Management (CRM) systems are evolving from rule-based workflow engines into intelligent, autonomous platforms powered by Artificial Intelligence (AI). AI-assisted CRM agents represent a transformative shift in how organizations manage sales, service, and support operations by enabling real-time decision-making, contextual automation, and personalized customer engagement at scale. These agents leverage machine learning, natural language processing (NLP), and predictive analytics to augment human agents rather than replace them, improving operational efficiency, customer satisfaction, and revenue outcomes. This paper presents a comprehensive examination of AI-assisted CRM agents, their system architecture, core functionalities across business functions, and their impact on modern enterprise operations.
The Future of Explainable AI on the Cloud
To guarantee the future of Explainable Artificial Intelligence (XAI) on the cloud, it is necessary to develop transparent, trustworthy, and scalable AI solutions that will connect complex models and human cognition. Since organizations are starting to adopt AI more and more via cloud computing systems such as AWS, Azure, and Google Cloud, it is necessary to incorporate explainability to ensure compliance, ethics, and accountability in decision-making. Cloud-based XAI is an easy-to-use, on-demand model interpretation, visualization, and bias detection tool with guaranteed data privacy and computational efficiency. The research and development of the future will be dedicated to the integration of explainability into automated pipelines to provide real-time insights and user-adaptive explainability based on the user role. As federated learning, edge computing, and regulatory systems emerge, cloud-based XAI will develop into hybrid systems that have a balanced interpretation, performance, and scale. In the long run, it will be a key to building the trust of the users, the responsible use of AI, and open decision-making in the various sectors.
From Detection to Provenance: A Deterministic Architecture for Authenticating AI Generated Content
Generative artificial intelligence systems are increasingly capable of producing text, images, audio, and video that are indistinguishable from human created content. While these advances enable innovation, they simultaneously erode traditional mechanisms for verifying authenticity. Existing mitigation strategies rely primarily on probabilistic detection models or optional watermarking schemes, both of which exhibit structural limitations. Detection models are reactive and degrade as generative models improve, while watermarking lacks universal enforcement and technical resilience. This paper argues that long term authenticity cannot depend on probabilistic inference alone. Instead, it proposes a deterministic cryptographic provenance framework termed the Authentication Architecture Layer. The architecture separates generation time signing, metadata construction, distribution, and independent verification into interoperable layers. By establishing authenticity at the point of content creation, the framework reduces reliance on retrospective detection. The paper analyzes societal, educational, economic, and governance implications, formalizes system assumptions and threat models, and outlines a research agenda for sustainable authenticity infrastructure.
Fraud–AML Convergence: Integrating Fraud and AML Detection, Shared Typologies, and Unified Case Management.
Fraud and anti–money laundering (AML) programs have historically evolved as separate control functions, often operating on different data, tools, and investigative workflows. However, converging threat landscapes where scams, synthetic identities, mule networks, account takeovers, and laundering chains intersect are forcing financial institutions to rethink fragmented detection and response models. This paper examines the concept of fraud–AML convergence as an integrated approach to identifying illicit activity across the full customer and transaction lifecycle. It analyzes how shared typologies (e.g., authorized push payment fraud feeding mule accounts, trade-based laundering enabled by invoice manipulation, and crypto-fiat layering via fraudulent onboarding) can be modeled through common risk signals, entity resolution, and network analytics. The study further evaluates architectural and operational options for integrating fraud and AML detection, including unified data layers, feature stores, shared rules-and-ML pipelines, and cross-domain alert prioritization. A central focus is unified case management: consolidating alerts, evidence, and investigator actions into a single workflow that reduces duplication, improves triage accuracy, and strengthens auditability and regulatory reporting. The paper also discusses key implementation challenges governance, explainability, privacy constraints, and model risk management alongside practical success metrics such as reduced false positives, faster time-to-disposition, and improved suspicious activity conversion. By presenting an integrated typology-to-workflow framework, this research offers a roadmap for institutions seeking more resilient, efficient, and intelligence-driven financial crime operations.
Continual and Lifelong Learning in Artificial Intelligence
Artificial intelligence systems have achieved remarkable performance in specialized tasks, yet most traditional models operate under a static learning paradigm: they are trained once on fixed datasets and then deployed without the ability to adapt continuously. In contrast, human intelligence evolves through ongoing experience, incrementally acquiring knowledge, refining skills, and integrating new information without catastrophically forgetting prior learning. Continual and lifelong learning in artificial intelligence seeks to replicate this adaptive capability by enabling models to learn sequentially from dynamic data streams while preserving previously acquired knowledge. This paradigm addresses critical limitations of conventional machine learning, including catastrophic forgetting, limited generalization across tasks, and inefficiency in data utilization. By integrating memory mechanisms, knowledge consolidation strategies, adaptive architectures, and transfer learning principles, continual learning systems aim to support scalable, flexible, and robust AI in real-world environments. This article presents a comprehensive and in-depth exploration of continual and lifelong learning, covering theoretical foundations, algorithmic approaches, system architectures, evaluation methodologies, application domains, and ethical considerations. It highlights the transformative potential of adaptive AI systems capable of sustained learning across time, domains, and tasks.
AI for Climate Modeling and Environmental Sustainability
Climate change and environmental degradation represent some of the most urgent and complex challenges facing humanity. Rising global temperatures, extreme weather events, biodiversity loss, ocean acidification, and resource depletion demand sophisticated analytical tools capable of understanding interconnected Earth systems. Traditional climate modeling approaches, grounded in physics-based simulations and large-scale numerical methods, have provided invaluable insights but face limitations in computational scalability, uncertainty quantification, and integration of heterogeneous data sources. Artificial intelligence (AI) has emerged as a transformative force in climate science and environmental sustainability by enabling advanced data-driven modeling, predictive analytics, optimization, and decision support. Through machine learning, deep learning, and hybrid AI-physics frameworks, researchers can enhance climate predictions, monitor environmental changes in real time, optimize renewable energy systems, and support sustainable resource management. This article presents a comprehensive and detailed exploration of AI for climate modeling and environmental sustainability, examining theoretical foundations, methodological innovations, computational frameworks, and real-world applications. It further discusses ethical considerations, data governance, interpretability challenges, and the environmental footprint of AI itself. By integrating computational intelligence with environmental science, AI offers powerful tools to accelerate climate research, inform policy decisions, and foster resilient and sustainable systems for the future.
Multi-Agent AI Systems for Automated HR Case Resolution and Workforce Analytics
A systems-oriented perspective of multi-agent systems (MAS) for automating human resources (HR) case resolution and workforce analytics is proposed. For automated HR case resolution, the design of the architectural framework within which the MAS operate is described, core automated case resolution processes are modelled, and agent methods, algorithms, and tools are specified. The architecture comprises functional modules and information repositories that model a cyber-physical representation of the enterprise. Processes within the module integrate data about the past, current, and future states of the enterprise and surrounding environment, use a case-based reasoning approach to diagnosis and problem resolution, and enable data-driven negotiation and continuous adaptation. An analysis of these sources, their interrelationships, and the methods for finance and workforce-related process automation provide the necessary foundation for using an MAS approach. Multi-agent systems perform and support workforce analytics, essentially an assembly of HR-related descriptive, predictive, and prescriptive analyses. The descriptive function integrates internal and external data sources and characterises the workforce in terms of historical and current relational and functional quality, quantity, geographical and organisational distribution, performance and development characteristics, and risk. The predictive function forecasts workforce depletion from normal retirement and turnover, as well as from abnormal circumstances such as illness, disability, natural disaster, and war. The prescriptive function recommends HR capability adjustment actions, as well as preventive and remedial measures to mitigate identified risks to workforce quality and capability. The general approach to automated HR case resolution has been validated with a pilot implementation applied to a typical problem.
Cloud-Integrated AI Chatbots for Automated HR Service Delivery Optimization
Cloud-integrated Artificial Intelligence (AI)-enabled chatbots can play a pivotal role in enhancing the service delivery and operational performance of Human Resources (HR) departments in organizations. The application of chatbots in HR has gained considerable traction recently, particularly as part of the shift to HR service delivery through common service centers. The cloud paradigm offers an excellent environment for a wide array of automated applications. Nevertheless, many existing implementations only partially automate specific functions. A cloud-integrated architecture allows for multiple HR functions to be automated through a single cloud-enabled chatbot, thereby optimizing resource utilization and service delivery. A cloud-integrated AI-enabled chatbot has been developed. The study specifically addresses the automation of employee onboarding and offboarding functions, with the solution being tested in a large enterprise environment. Evaluation metricsincluding efficiency, accuracy, and response time are then presented. Additionally, risk-management measures are discussed with a particular focus on fairness, bias, and transparency.
Hybrid CNN–LSTM Models for Cross-Project Fault Prediction: Robust Generalization Under Dataset Shift
Abstract: Cross-project fault prediction (CPFP) aims to learn defect predictors from one or more source projects and deploy them on a different target project where labels may be unavailable, incomplete, delayed, or noisy. Despite decades of progress in software defect prediction, generalization in CPFP remains brittle because target projects commonly exhibit dataset shift: changes in metric distributions, process behaviors, architectural patterns, operational platforms, and data pipelines that violate the implicit i.i.d. assumptions of standard supervised learning. This manuscript proposes a shift-aware hybrid CNN–LSTM framework tailored to CPFP under realistic dataset shift. The model combines convolutional feature extractors (for local, interaction-level patterns among engineered metrics or change descriptors) with recurrent sequence modeling (for temporal and cross-change dependencies), and integrates training strategies that stabilize performance under covariate and conditional shift. We formalize the CPFP learning objective under shift, define a practical shift taxonomy grounded in modern enterprise software contexts, and introduce a training pipeline using robust scaling, project-conditional normalization, and shift-mitigation regularization. The approach is designed for operational feasibility: it supports constrained edge environments and embedded telemetry streams, aligns with CI/CD governance processes, and incorporates explainability requirements for high-stakes domains. A worked example illustrates how dataset shift can invert model rankings across projects and how shift-aware training reduces this volatility. The manuscript concludes with a reproducible evaluation protocol, threat analysis, and deployment guidance for fault prediction in cloud-native and regulated settings.
AI-Powered Intelligent IVR in Healthcare
Artificial intelligence (AI) is rapidly transforming the delivery of care, and its application to the contact center is one of the most scalable and common opportunities to improve patient access to care. The article outlines the limitations of legacy Interactive Voice Response (IVR) systems, the root causes of that dissatisfaction and the operational inefficiency they have generated for decades, and a framework for a conversational AI-powered IVR system tailor-made for healthcare. The article describes how the system architecture, natural language processing, contextual dialogue management, and sentiment detection and analysis make the system more responsive to patient behavior and how the system performs on key functions such as appointment scheduling, medication reconciliation, and claims management and processing. The article also makes the case that compliance and governance are equally as important as biomedical and technical issues and that privacy protection, algorithmic fairness, and ethical accountability are mandatory in the deployment of AI-enabled systems. The article then discusses future research needed to produce evidence on real-world deployment and equitable access to the technology․
Reinforcement Learning Applications for Dynamic Pricing in Microsoft Dynamics 365 Commerce
Dynamic pricing has become a critical capability in modern digital commerce platforms, enabling organizations to optimize revenue, profit margins, and inventory turnover in rapidly changing market environments. Traditional rule-based and statistical pricing models often fail to adapt effectively to complex and dynamic consumer behavior. This paper explores the application of reinforcement learning (RL) techniques for dynamic pricing within Microsoft Dynamics 365 Commerce. The study formulates the pricing process as a Markov Decision Process (MDP), where pricing decisions are treated as sequential actions that influence future demand and revenue outcomes. Various reinforcement learning approaches, including contextual multi-armed bandits and Deep Q-Networks, are evaluated for their suitability in enterprise retail environments. The proposed framework integrates RL models with the existing pricing engine of Microsoft Dynamics 365 Commerce using API-based overrides and cloud-based machine learning services. Experimental evaluation demonstrates that reinforcement learning-driven pricing strategies outperform static and rule-based pricing methods in terms of revenue uplift, margin optimization, and inventory efficiency. The paper also discusses implementation challenges such as regulatory compliance, fairness considerations, cold-start problems, and real-time system constraints. The findings suggest that reinforcement learning can provide a scalable and adaptive pricing solution for enterprise commerce systems, offering significant competitive advantage in digital retail ecosystems.
Governing Enterprise AI at Scale: from Model Risk Management to System Level Intelligence Assurance
As enterprises transition from single model deployments to complex multi agent AI ecosystems, governance practices that focus narrowly on individual models (e.g., documentation, validation, and monitoring of model risk) prove insufficient. Failures now emerge from system level dynamics context loss across agents, inconsistent reasoning, untraceable decision lineage, and confidence drift under fragmented data estates. This paper advances a system level intelligence assurance framework that elevates governance from policy and process to architecture and telemetry. We outline a reference approach that combines shared memory, validation loops, confidence governance, policy tagging, and auditable evidence chains with operational KPIs such as reasoning consistency, lineage completeness, and redundant compute rate. Implementation patterns, adoption playbooks, and cross industry use cases demonstrate how enterprises can move beyond model risk management to govern the entire AI system ensuring decisions are explainable, compliant, resilient, and continuously improving. The proposed approach complements Quality Engineering by making trust executable and measurable in production.
The Convergence of Data Virtualization and Federated Learning in Pharmaceutical Real-World Evidence (RWE) Generation: A Survey and Gap Research in Architectures, Tools, and Governance Challenges
The generation of pharmaceutical Real-World Evidence (RWE) is a critical imperative for modern healthcare, yet it is hindered by two fundamental bottlenecks: (1) stringent privacy regulations, such as HIPAA, which preclude data centralization, and (2) pervasive, systemic data fragmentation within healthcare institutions.2 Federated Learning (FL) has emerged as the consensus solution for the privacy challenge, enabling model training on distributed data. Concurrently, Data Virtualization (DV) is the industry-standard solution for data fragmentation, providing a unified logical view of data silos. The current scientific literature, however, investigates these two solutions in parallel, failing to address the critical gap at their intersection. FL research implicitly operates on a "Unified Node Assumption," presuming each participating hospital has its data in a single, queryable repository, which is practically false. This paper bridges this gap by conducting a systematic survey of both domains and proposing the first architectural taxonomy of "Federated Learning on a Virtualized Data Layer" (FL-on-VD). Three novel architectural models are proposed and illustrated: (1) FL with Virtualized Query Pushing (FL-VQP), (2) FL on a Logical Data Mesh (FL-LDM), and (3) FL on a Centralized Virtual-View (FL-CVV). This paper analyzes the unique, second-order challenges this convergence creates in federated query optimization, semantic interoperability, and "double-blind" governance. It concludes that this unified FL-on-VD architecture represents the only viable and scalable path toward national-level RWE generation.
Federated Edge-Centric Observability for Multi-Cloud Healthcare Data Pipelines
Healthcare organizations increasingly operate data pipelines that span multiple public clouds and on-premise environments, integrating electronic health records (EHRs), claims, and Internet of Medical Things (IoMT) telemetry into shared analytic platforms. In this context, traditional centralized monitoring and data quality approaches—which assume that logs and data can be aggregated into a single platform—are costly and fragile. Moving protected health information (PHI) across cloud boundaries for inspection inflates egress spend and raises regulatory concerns, while platform-specific observability tools provide limited visibility into how failures propagate end-to-end across heterogeneous environments. This paper proposes a federated, edge-centric observability architecture for multi-cloud healthcare data pipelines. ssssLightweight observability agents are deployed near data sources in each cloud and on-premise region to instrument pipelines, compute local health and anomaly metrics, and evaluate policies. Above them, a cloud-agnostic Quality-as-a-Service (QaaS) control plane aggregates derived signals, manages models and policies, and orchestrates human-in-the-loop remediation using secure pointers rather than PHI replication. The architecture explicitly decouples the data plane from a quality and observability plane, enabling cross-cloud visibility while preserving data sovereignty. We evaluate the design on a synthetic, FHIR-compatible multi-cloud healthcare workload comprising 47 pipelines across four environments over 30 simulated days. Using chaos engineering, we inject realistic failure modes, including schema changes, delayed or partial loads, cross-cloud link failures, and backlogged batch processing. We compare the proposed federated approach to a representative centralized monitoring baseline along three dimensions: time-to-detection and localization of failures, monitoring-related inter-cloud data transfer costs, and resource overhead of agents and control plane. Across 216 chaos injections, the edge-centric architecture reduces median time-to-detection and localization while lowering monitoring-related egress volume by roughly 25% in this synthetic workload, with agent overhead of about 4–7% CPU and ~3% memory on batch pipelines. Given the limitations of synthetic data and simplified cost modeling, these results should be interpreted as early-stage validation of a promising architectural pattern rather than definitive benchmarks.
AI-Enabled Business Process Automation in Enterprise Healthcare Systems: Architectural and Governance Frameworks
The healthcare industry is going through major changes as organizations start combining business process tools with artificial intelligence (AI). This paper looks at how the Appian platform helps manage and automate important tasks in healthcare settings. Appian brings together low-code development tools, connected data systems, and smart document processing. These features help hospitals and healthcare providers deal with common problems such as scattered data, too much paperwork, and staff burnout. By connecting systems and automating routine work, healthcare teams can save time and focus more on patient care. The paper also explains how AI can support tasks like patient triage, where cases are prioritized based on urgency, and revenue cycle management, which handles billing and payments. It discusses how healthcare systems use standard technologies like HL7 FHIR® to make sure different platforms can share information smoothly. Furthermore, the paper outlines five key AI design approaches that help organizations use AI responsibly. These approaches make sure patient data stays protected and that systems follow regulations such as HIPAA. Overall, the study shows that combining AI with business process management helps healthcare organizations move from simply reacting to problems toward planning ahead and delivering more patient-centered care.
Reducing Fraud Leakage Using Machine Learning-Based Behavioral Pattern Analysis
The rapid proliferation of digital finance and e-commerce has catalyzed a corresponding surge in sophisticated cybercrime, rendering traditional security frameworks increasingly obsolete. This white paper examines the critical challenge of "fraud leakage" the volume of fraudulent activity that bypasses established defenses and proposes a transition toward Machine Learning (ML)-based behavioral pattern analysis. By moving from reactive, rule-based systems (RBS) to proactive, entity-centric behavioral monitoring, financial institutions can identify subtle anomalies in user interaction, temporal signatures, and navigation entropy that preceded material loss. This research synthesizes findings from supervised frameworks like XGBoost, optimized via SMOTE-ENN resampling, and unsupervised deep learning models such as Autoencoders integrated with Adaptive Reconstruction Threshold (ART) mechanisms. The study highlights how behavioral biometrics, including keystroke dynamics and gesture curvature, provide a robust shield against synthetic identity fraud and deep-fake driven account takeovers. Ultimately, the paper argues that the future of fraud prevention lies in hybrid architectures that balance detection precision with real-time operational efficiency and ethical governance, ensuring compliance with global mandates such as GDPR and PCI-DSS while maintaining a frictionless user experience.
Real-Time Energy and Thermal Optimization in Electric Delivery Fleets via Edge-Based Forecasting and Deep Q-Learning
As e-commerce has seen tremendous growth, it has put extreme pressure on last-mile delivery systems. Although electric vehicles (EVs) have several positive attributes from a sustainability standpoint, they have energy inefficiencies when dealing with dense urban environments. Due to the nature of EVs operating in urban areas, they run into frequent stop-and-go cycles (creating lower-quality, more inefficient operation of battery charging), and because recovery cycles due to regenerative braking happen in unpredictable bursts, the thermal stress that occurs on the battery during these operations accelerates battery degradation. Also, the latency associated with cloud routing creates high latency that makes it unreasonable for quick adaptations needed for real-time applications. This research study presents a novel hybrid edge-intelligent framework that combines the use of Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting with that of an edge-deployed Deep Q-Network (DQN) reinforcement learning agent capable of optimizing battery thermal behaviors and optimizing energy consumption. For example, the SARIMA data is largely used to predict traffic density around an EV and its battery usage pattern (use of energy and charging demand) over short durations, while the DQN can make real-time charging and routing decisions within an average of 50 milliseconds or less. A synthetic dataset created from SUMO (Simulation of Urban Mobility) applying physics-based battery thermal dynamics demonstrates that there were energy savings on average of 27% and increased projected battery cycle life of 19% over the energy consumption and battery cycle life achieved using a rule-based benchmark. Further investigations expand on the full architecture, mathematical modeling, and trade-offs in applying edge computing to achieve scalable optimization of EV fleets.
Small Language Models and Neuro-Symbolic AI in Zonal Architectures: Federated Low-Rank Adaptation (Fed-LoRA) for Regional Behavior Modeling
The automotive industry is undergoing a fundamental change, shifting from domain-centric Electrical/Electronic (E/E) architectures to Zonal Architectures designed for the Software-Defined Vehicle (SDV). This shift enables high-performance edge computing but imposes strict constraints on power, thermal management, and resource contention for mixed-criticality tasks. At the same time, autonomous systems must adapt to diverse regional driving habits ranging from strict traffic-law compliance in Western Europe to the chaotic, negotiation-heavy traffic of South Asia posing challenges for existing centralized training approaches. This report introduces a comprehensive framework that combines Small Language Models (SLMs) for semantic reasoning, Neuro-Symbolic AI (NeSy) for safety validation, and Federated Low-Rank Adaptation (Fed-LoRA) for bandwidth-efficient, privacy-preserving ongoing learning. We examine the hardware capabilities of advanced zonal controllers (e.g., NXP S32G3, TI Jacinto 7) and show that, despite having less raw data-processing power than data center GPUs, their NPU-accelerated designs are adequate for quantized SLMs when optimized with techniques such as Federated Silver Bullet (Fed-SB). Additionally, we propose a hierarchical "Regional Behavior" learning model in which a Neuro-Symbolic safety shield ensures that universal traffic rules are followed, while the SLM adapts to local cultural norms via low-rank parameter updates. This hybrid architecture balances the need for local customization with the essential safety guarantees required in next-generation autonomous vehicle mobility.
Federated Low-Rank Adaptation (Fed-LoRA)
LEAIM: A Layered Enterprise Architecture Model for Scalable and Governed Integration of Artificial Intelligence in Distributed Systems
The integration of Artificial Intelligence (AI) models into enterprise-scale distributed systems introduces architectural challenges that extend beyond model training and algorithmic optimization. While contemporary research emphasizes model accuracy and computational efficiency, system-level integration within complex enterprise environments remains insufficiently formalized. Existing approaches frequently embed AI components directly within business services or centralize inference without enforcing architectural separation, resulting in tight coupling, limited scalability, and governance vulnerabilities. This paper introduces the Layered Enterprise Artificial Intelligence Integration Model (LEAIM), a structured enterprise architecture framework for scalable and governed AI integration in distributed systems. LEAIM defines five formally separated layersdata acquisition, model lifecycle management, model serving, orchestration, and governanceand enforces explicit dependency constraints to prevent cross-layer coupling. The model incorporates scalability modeling, latency decomposition, security isolation, and lifecycle governance as primary architectural properties. Comparative evaluation demonstrates that LEAIM improves modularity, resilience, governance coverage, and fault containment relative to embedded and centralized integration patterns. By elevating AI deployment from ad-hoc implementation to formal architectural design, LEAIM contributes a reusable enterprise framework for sustainable AI system integration.
Layered Enterprise AI Integration Model (LEAIM)